Network meta-analysis of survival data with fractional polynomials.
Network meta-analysis of survival data with fractional polynomials.
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DOI：
10.1186/1471-2288-11-61
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发表时间：
2011-05-06
影响因子：
4
通讯作者：
Jansen JP
中科院分区：
文献类型：
--
作者：
Jansen JP
总体水平生存数据的成对荟萃分析、间接治疗比较和网络荟萃分析通常基于报告的风险比，其依赖于比例风险假设。当风险函数相交时，这种假设是不可信的，并且可能对基于预期生存比较的决策产生巨大影响，例如成本效益分析。作为一种替代网络荟萃分析的生存数据，其中的治疗效果是由恒定的风险比，多维治疗效果的方法。使用分数多项式对随机对照试验中比较的干预措施的风险函数进行建模，并在研究中合成（并间接比较）试验中这些分数多项式参数之间的差异。所提出的模型说明了在非小细胞肺癌的生存数据的分析。评价了一阶和二阶分数多项式的固定和随机效应。与基于恒定风险比的荟萃分析相比，使用分数多项式用几个参数表示治疗效果的模型对生存数据进行（网络）荟萃分析，可以更接近于现有数据。
Pairwise meta-analysis, indirect treatment comparisons and network meta-analysis for aggregate level survival data are often based on the reported hazard ratio, which relies on the proportional hazards assumption. This assumption is implausible when hazard functions intersect, and can have a huge impact on decisions based on comparisons of expected survival, such as cost-effectiveness analysis. As an alternative to network meta-analysis of survival data in which the treatment effect is represented by the constant hazard ratio, a multi-dimensional treatment effect approach is presented. With fractional polynomials the hazard functions of interventions compared in a randomized controlled trial are modeled, and the difference between the parameters of these fractional polynomials within a trial are synthesized (and indirectly compared) across studies. The proposed models are illustrated with an analysis of survival data in non-small-cell lung cancer. Fixed and random effects first and second order fractional polynomials were evaluated. (Network) meta-analysis of survival data with models where the treatment effect is represented with several parameters using fractional polynomials can be more closely fitted to the available data than meta-analysis based on the constant hazard ratio.
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